Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem
Soils represent the largest store of carbon in the biosphere with soils at high latitudes containing twice as much carbon (C) than the atmosphere. High latitude tundra vegetation communities show increases in the relative abundance and cover of deciduous shrubs which may influence net ecosystem exch...
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Online Access: | https://doi.org/10.3390/rs13132571 |
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ftmdpi:oai:mdpi.com:/2072-4292/13/13/2571/ 2023-08-20T03:59:02+02:00 Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem Olivia Azevedo Thomas C. Parker Matthias B. Siewert Jens-Arne Subke agris 2021-06-30 application/pdf https://doi.org/10.3390/rs13132571 EN eng Multidisciplinary Digital Publishing Institute Biogeosciences Remote Sensing https://dx.doi.org/10.3390/rs13132571 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 13; Pages: 2571 Abisko CO 2 flux LAI modelling plant functional type SOC vegetation index Text 2021 ftmdpi https://doi.org/10.3390/rs13132571 2023-08-01T02:05:30Z Soils represent the largest store of carbon in the biosphere with soils at high latitudes containing twice as much carbon (C) than the atmosphere. High latitude tundra vegetation communities show increases in the relative abundance and cover of deciduous shrubs which may influence net ecosystem exchange of CO2 from this C-rich ecosystem. Monitoring soil respiration (Rs) as a crucial component of the ecosystem carbon balance at regional scales is difficult given the remoteness of these ecosystems and the intensiveness of measurements that is required. Here we use direct measurements of Rs from contrasting tundra plant communities combined with direct measurements of aboveground plant productivity via Normalised Difference Vegetation Index (NDVI) to predict soil respiration across four key vegetation communities in a tundra ecosystem. Soil respiration exhibited a nonlinear relationship with NDVI (y = 0.202e3.508 x, p < 0.001). Our results further suggest that NDVI and soil temperature can help predict Rs if vegetation type is taken into consideration. We observed, however, that NDVI is not a relevant explanatory variable in the estimation of SOC in a single-study analysis. Text Abisko Arctic Tundra MDPI Open Access Publishing Arctic Abisko ENVELOPE(18.829,18.829,68.349,68.349) Remote Sensing 13 13 2571 |
institution |
Open Polar |
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MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
Abisko CO 2 flux LAI modelling plant functional type SOC vegetation index |
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Abisko CO 2 flux LAI modelling plant functional type SOC vegetation index Olivia Azevedo Thomas C. Parker Matthias B. Siewert Jens-Arne Subke Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem |
topic_facet |
Abisko CO 2 flux LAI modelling plant functional type SOC vegetation index |
description |
Soils represent the largest store of carbon in the biosphere with soils at high latitudes containing twice as much carbon (C) than the atmosphere. High latitude tundra vegetation communities show increases in the relative abundance and cover of deciduous shrubs which may influence net ecosystem exchange of CO2 from this C-rich ecosystem. Monitoring soil respiration (Rs) as a crucial component of the ecosystem carbon balance at regional scales is difficult given the remoteness of these ecosystems and the intensiveness of measurements that is required. Here we use direct measurements of Rs from contrasting tundra plant communities combined with direct measurements of aboveground plant productivity via Normalised Difference Vegetation Index (NDVI) to predict soil respiration across four key vegetation communities in a tundra ecosystem. Soil respiration exhibited a nonlinear relationship with NDVI (y = 0.202e3.508 x, p < 0.001). Our results further suggest that NDVI and soil temperature can help predict Rs if vegetation type is taken into consideration. We observed, however, that NDVI is not a relevant explanatory variable in the estimation of SOC in a single-study analysis. |
format |
Text |
author |
Olivia Azevedo Thomas C. Parker Matthias B. Siewert Jens-Arne Subke |
author_facet |
Olivia Azevedo Thomas C. Parker Matthias B. Siewert Jens-Arne Subke |
author_sort |
Olivia Azevedo |
title |
Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem |
title_short |
Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem |
title_full |
Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem |
title_fullStr |
Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem |
title_full_unstemmed |
Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem |
title_sort |
predicting soil respiration from plant productivity (ndvi) in a sub-arctic tundra ecosystem |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13132571 |
op_coverage |
agris |
long_lat |
ENVELOPE(18.829,18.829,68.349,68.349) |
geographic |
Arctic Abisko |
geographic_facet |
Arctic Abisko |
genre |
Abisko Arctic Tundra |
genre_facet |
Abisko Arctic Tundra |
op_source |
Remote Sensing; Volume 13; Issue 13; Pages: 2571 |
op_relation |
Biogeosciences Remote Sensing https://dx.doi.org/10.3390/rs13132571 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs13132571 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
13 |
container_start_page |
2571 |
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1774717289472458752 |